Experimental demonstration of highly reliable dynamic memristor for artificial neuron and neuromorphic computing

See-On Park, Hakcheon Jeong, Jongyong Park, Jongmin Bae and Shinhyun Choi ()
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See-On Park: Korea Advanced Institute of Science and Technology (KAIST)
Hakcheon Jeong: Korea Advanced Institute of Science and Technology (KAIST)
Jongyong Park: Korea Advanced Institute of Science and Technology (KAIST)
Jongmin Bae: Korea Advanced Institute of Science and Technology (KAIST)
Shinhyun Choi: Korea Advanced Institute of Science and Technology (KAIST)

Nature Communications, 2022, vol. 13, issue 1, 1-13

Abstract: Abstract Neuromorphic computing, a computing paradigm inspired by the human brain, enables energy-efficient and fast artificial neural networks. To process information, neuromorphic computing directly mimics the operation of biological neurons in a human brain. To effectively imitate biological neurons with electrical devices, memristor-based artificial neurons attract attention because of their simple structure, energy efficiency, and excellent scalability. However, memristor’s non-reliability issues have been one of the main obstacles for the development of memristor-based artificial neurons and neuromorphic computings. Here, we show a memristor 1R cross-bar array without transistor devices for individual memristor access with low variation, 100% yield, large dynamic range, and fast speed for artificial neuron and neuromorphic computing. Based on the developed memristor, we experimentally demonstrate a memristor-based neuron with leaky-integrate and fire property with excellent reliability. Furthermore, we develop a neuro-memristive computing system based on the short-term memory effect of the developed memristor for efficient processing of sequential data. Our neuro-memristive computing system successfully trains and generates bio-medical sequential data (antimicrobial peptides) while using a small number of training parameters. Our results open up the possibility of memristor-based artificial neurons and neuromorphic computing systems, which are essential for energy-efficient edge computing devices.

Date: 2022
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DOI: 10.1038/s41467-022-30539-6

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